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A Bayesian Quantile Modeling for Spatiotemporal Relative Risk: An Application to Adverse Risk Detection of Respiratory Diseases in South Carolina, USA

1
Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand
2
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(9), 2042; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15092042
Received: 30 July 2018 / Revised: 11 September 2018 / Accepted: 15 September 2018 / Published: 18 September 2018
Quantile modeling has been seen as an alternative and useful complement to ordinary regression mainly focusing on the mean. To directly apply quantile modeling to areal data the discrete conditional quantile function of the data can be an issue. Although jittering by adding a small number from a uniform distribution to impose pseudo-continuity has been proposed, the approach can have a great influence on responses with small values. Thus we proposed an alternative to model the quantiles of relative risk for spatiotemporal areal health data within a Bayesian framework using the log-Laplace distribution. A simulation study was conducted to assess the performance of the proposed method and examine whether the model could robustly estimate quantiles of spatiotemporal count data. To perform a test with a real data example, we evaluated the potential application of clustering under the proposed log-Laplace and mean regression. The data were obtained from the total number of emergency room discharges for respiratory conditions, both infectious and non-infectious diseases, in the U.S. state of South Carolina in 2009. From both simulation and case studies, the proposed quantile modeling demonstrated potential for broad applicability in various areas of spatial health studies including anomaly detection. View Full-Text
Keywords: quantile modeling; spatiotemporal; log Laplace; Bayesian; adverse risk detection; respiratory disease quantile modeling; spatiotemporal; log Laplace; Bayesian; adverse risk detection; respiratory disease
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MDPI and ACS Style

Rotejanaprasert, C.; Lawson, A.B. A Bayesian Quantile Modeling for Spatiotemporal Relative Risk: An Application to Adverse Risk Detection of Respiratory Diseases in South Carolina, USA. Int. J. Environ. Res. Public Health 2018, 15, 2042. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15092042

AMA Style

Rotejanaprasert C, Lawson AB. A Bayesian Quantile Modeling for Spatiotemporal Relative Risk: An Application to Adverse Risk Detection of Respiratory Diseases in South Carolina, USA. International Journal of Environmental Research and Public Health. 2018; 15(9):2042. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15092042

Chicago/Turabian Style

Rotejanaprasert, Chawarat; Lawson, Andrew B. 2018. "A Bayesian Quantile Modeling for Spatiotemporal Relative Risk: An Application to Adverse Risk Detection of Respiratory Diseases in South Carolina, USA" Int. J. Environ. Res. Public Health 15, no. 9: 2042. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15092042

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